# AIRoA MoMa Dataset: A Large-Scale Hierarchical Dataset for Mobile Manipulation

arXiv: https://arxiv.org/abs/2509.25032 · HTML: https://mlsanity.com/paper/2509.25032/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 15 references that are in the map.
- First posted on arXiv 29 Sep 2025.

## Builds on (most cited first, 11 of 11)

- [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://mlsanity.com/paper/2307.15818/) (2023)
- [RT-1: Robotics Transformer for Real-World Control at Scale](https://mlsanity.com/paper/2212.06817/) (2022)
- [Open X-Embodiment: Robotic Learning Datasets and RT-X Models](https://mlsanity.com/paper/2310.08864/) (2023)
- [DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset](https://mlsanity.com/paper/2403.12945/) (2024)
- [BridgeData V2: A Dataset for Robot Learning at Scale](https://mlsanity.com/paper/2308.12952/) (2023)
- [Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation](https://mlsanity.com/paper/2401.02117/) (2024)
- [RoboNet: Large-Scale Multi-Robot Learning](https://mlsanity.com/paper/1910.11215/) (2019)
- [GELLO: A General, Low-Cost, and Intuitive Teleoperation Framework for Robot Manipulators](https://mlsanity.com/paper/2309.13037/) (2023)
- [RH20T: A Comprehensive Robotic Dataset for Learning Diverse Skills in One-Shot](https://mlsanity.com/paper/2307.00595/) (2023)
- [Baxter's Homunculus: Virtual Reality Spaces for Teleoperation in Manufacturing](https://mlsanity.com/paper/1703.01270/) (2017)
- [World Robot Challenge 2020 -- Partner Robot: A Data-Driven Approach for Room Tidying with Mobile Manipulator](https://mlsanity.com/paper/2207.10106/) (2022)

## Built on by (most cited first, 5 of 5)

- [OSCAR: Omni-Embodiment Action-Conditioned World Model for Robotics](https://mlsanity.com/paper/2606.04463/) (2026)
- [AnoleVLA: Lightweight Vision-Language-Action Model with Deep State Space Models for Mobile Manipulation](https://mlsanity.com/paper/2603.15046/) (2026)
- [HiFlow: Tokenization-Free Scale-Wise Autoregressive Policy Learning via Flow Matching](https://mlsanity.com/paper/2603.27281/) (2026)
- [When Absolute State Fails: Evaluating Proprioceptive Encodings for Robust Manipulation](https://mlsanity.com/paper/2605.13067/) (2026)
- [Kepler-Encoder-v0.1: Towards a Multimodal Embedding Model for Robots](https://mlsanity.com/paper/2607.13522/) (2026)

## Abstract (arXiv)

As robots transition from controlled settings to unstructured human environments, building generalist agents that can reliably follow natural language instructions remains a central challenge. Progress in robust mobile manipulation requires large-scale multimodal datasets that capture contact-rich and long-horizon tasks, yet existing resources lack synchronized force-torque sensing, hierarchical annotations, and explicit failure cases. We address this gap with the AIRoA MoMa Dataset, a large-scale real-world multimodal dataset for mobile manipulation. It includes synchronized RGB images, joint states, six-axis wrist force-torque signals, and internal robot states, together with a novel two-layer annotation schema of sub-goals and primitive actions for hierarchical learning and error analysis. The initial dataset comprises 25,469 episodes (approx. 94 hours) collected with the Human Support Robot (HSR) and is fully standardized in the LeRobot v2.1 format. By uniquely integrating mobile manipulation, contact-rich interaction, and long-horizon structure, AIRoA MoMa provides a critical benchmark for advancing the next generation of Vision-Language-Action models. The first version of our dataset is now available at https://huggingface.co/datasets/airoa-org/airoa-moma .